PartialDependence#
Link to Algorithm description: Partial Dependence Plot
- class interpret.blackbox.PartialDependence(model, data, feature_names=None, feature_types=None, num_points=10, std_coef=1.0)#
Partial dependence plots as defined in Friedman’s paper on “Greedy function approximation: a gradient boosting machine”.
Friedman, Jerome H. “Greedy function approximation: a gradient boosting machine.” Annals of statistics (2001): 1189-1232.
Initializes class.
- Parameters:
model – model or prediction function of model (predict_proba for classification or predict for regression)
data – Data used to initialize PartialDependence with.
feature_names – List of feature names.
feature_types – List of feature types.
num_points – Number of grid points for the x axis.
std_coef – Co-efficient for standard deviation.
- explain_global(name=None)#
Provides approximate global explanation for blackbox model.
- Parameters:
name – User-defined explanation name.
- Returns:
An explanation object, visualizes dependence plots.